Cai, Hanzhe (2024) Bridge Pier Surface Defect Detection Based on Improved YOLOV9. Masters thesis, University of Wales Trinity Saint David.
|
Text
Cai_H_MSc_Thesis.pdf - Accepted Version Available under License CC-BY-NC-ND Creative Commons Attribution Non-commercial No Derivatives. Download (3MB) | Preview |
Abstract
This research introduces an innovative surface defect detection methodology specifically designed for bridge piers, which integrates state-of-the-art image enhancement algorithms with sophisticated target detection frameworks. This hybrid approach effectively addresses some of the inherent limitations observed in existing deep learning-based defect detection methodologies, particularly under conditions of suboptimal image quality and challenges related to the detection of minute targets. Comparative results demonstrate that this novel technique achieves a 3.9% increase in the mean Average Precision (mAP50) over the baseline model. Furthermore, this is accomplished with a reduction in model complexity, as evidenced by a 9.8% decrease in the number of parameters and a substantial reduction in computational demand, quantified as a 7.5 GFLOPS decrease. This study not only advances the field of structural health monitoring but also enhances the operational efficiency of automated defect detection systems.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Theses and Dissertations > Masters Dissertations |
| Depositing User: | Victoria Hankinson |
| Date Deposited: | 09 Jan 2025 15:46 |
| Last Modified: | 28 Oct 2025 10:46 |
| URI: | https://repository.uwtsd.ac.uk/id/eprint/3308 |
Administrator Actions (login required)
 |
Edit Item - Repository Staff Only |